GA-based Automatic Test Data Generation for UML State Diagrams with Parallel Paths

Doungsa-ard, Chartchai; Dahal, Keshav; Hossain, Alamgir; Suwannasart, Taratip

doi:10.1007/978-1-84800-241-8_16

Cited by 21 publications

(11 citation statements)

References 10 publications

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“…At the same time, testing is the most time consuming and expensive process in software construction [1] [2]. Almost 40% of the projects' budget, time, and effort spent on testing the software system [3].The development team faces multiple obstacles and impediments in completing the software project demanded by the customer within the estimated cost and stipulated time. With increased rapid development methods and customer demands, it becomes necessary to deliverthe software project with a minimal defect.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Test Cases: A Meta-Heuristic Approach

Prakash¹

2020

IJATCSE

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Software testing is considered as one of the most challenging and time consuming activity involved in the software development process. Testing involves the creation of test cases that helps to explore the defects present in the software system. Test cases play a major role in the testing process to uncover the critical errors that exist in the system. Thus, it is necessary to prioritize test cases for effective testing. This paper aims at performing a comparative study for the optimization of test cases involved in software development using Meta-heuristic techniques. The weightage of each test case is determined,which helps in optimizing the test cases. The performance is evaluated by comparing two metaheuristic algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).The result shows PSO outperforms GA in terms of accuracy, error rate, and execution time for the chosen test case optimization problem.

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Section: Introductionmentioning

confidence: 99%

Optimization of Test Cases: A Meta-Heuristic Approach

Prakash¹

2020

IJATCSE

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“…The articles propose to use genetic algorithms for generating triggers for UML diagrams, which will allow finding the critical path in the program. In article [19] an improved genetic algorithm-based method is proposed for selecting test data for multiple parallel paths in UML diagrams.…”

Section: Introductionmentioning

confidence: 99%

Using genetic algorithm for generating optimal data sets to automatic testing the program code

Serdyukov¹,

Avdeenko²

2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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In present paper we propose an approach to automatic generation of test data set based on application of the genetic algorithm. We consider original procedure for computation of the weights of code operations used to formulate the fitness function being the sum of these weights. Terminal objective and result of fitness function selection is maximization of code coverage by generated test data set. The idea of the genetic algorithm application approach is that first we choose the most complex branches of the program code for accounting in the fitness function. After taking the branch into account its weight is reset to zero in order to ensure maximum code coverage. By adjusting the algorithm, it is possible to ensure that the automatic test data generating algorithm finds the most distant from each other parts of the program code and, thus, the higher level of code coverage is attained. We give a detailed example illustrating the work and advantages of considered approach and suppose further improvements of the method.

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“…Test paths are extracted from a graph that is constructed based on software specifications. Generating test paths from Unified Modeling Language (UML) diagrams help developers to reduce their efforts for learning new specifications so much because it is a mainstream technique in software development [1]. On the other hand, UML diagrams that are made in the design phase are useful sources for white box test case generation [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Metaheuristic techniques are used both to get a complete coverage over test results and to have an appropriate size of the test set [13]. These techniques include Ant Colony Optimization algorithm [14][15][16][17], Genetic Algorithm [1], and other searchbased test data generation methods [18]. But a few researches are focused on test path generation.…”

Section: Introductionmentioning

confidence: 99%

Automatic test path generation from sequence diagram using genetic algorithm

Hoseini

Jalili

2014

7'th International Symposium on Telecommunications (IST'2014)

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Software testing is an important and complicated phase of software development cycle. Software test process acquires test cases as input for the system under test to evaluate the behavior of the product. If test cases are prepared before coding, it will help the developers to control their code to conform to specification. White box testing requires a set of predefined test paths to generate test cases, therefore generating a set of reliable test paths is a critical task. The most common approach in white box testing is to generate test paths from source code while the generation process must be delayed until completion of source code. Using sequence diagram as an input artifact for generating test path is cost and time efficient due to the fact that test process starts before implementation phase. Furthermore, tester involvement in source code complexity is reduced to a minimum. Test paths are generated from the control flow graph, which is extracted from sequence diagrams. Among all graph based coverage criteria, Prime path coverage subsumes different graph based coverage criteria that lead us to complete path coverage. Also, prime path coverage concentrates on visiting all nodes and edges in the control flow graph rather than traversing all existing paths, which results in test effort reduction. Genetic algorithm is applied minimize the number of test cases required to reach the highest coverage.In this paper, we proposed a model to generate all prime paths automatically and extract minimum paths with shortest possible length, which covers all prime paths by means of genetic algorithm. The experimental results show the generated paths can easily turn into optimal test paths with the best prime path coverage having the least number of test paths.

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GA-based Automatic Test Data Generation for UML State Diagrams with Parallel Paths

Cited by 21 publications

References 10 publications

Optimization of Test Cases: A Meta-Heuristic Approach

Optimization of Test Cases: A Meta-Heuristic Approach

Using genetic algorithm for generating optimal data sets to automatic testing the program code

Automatic test path generation from sequence diagram using genetic algorithm

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